> For the complete documentation index, see [llms.txt](https://cortex-docs.paloaltonetworks.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cortex-docs.paloaltonetworks.com/cortex-cloud-posture-management/cloud-inventory-and-environment/asset-classes/ai-assets.md). # AI assets Cortex Cloud provides a comprehensive overview of the AI assets within an organization, designed to ensure AI security by offering tools to review and prioritize AI risks effectively. ### AI assets inventory You can view all AI assets in your environment, regardless of deployment mode or cloud provider. Navigate to **Inventory** → **All Assets** → **AI** to access the inventory. The top of the page features interactive widgets that summarize your AI security posture: * **Assets at risk:** The number of AI assets with detected vulnerabilities or misconfigurations. * **Cloud provider breakdown:** A summary of AI resources distributed across your cloud environments (AWS, Azure, GCP). * **Sensitive assets:** The number of AI assets handling classified or sensitive data. * **Sensitive assets discovered last 7 days:** A trend metric for newly exposed sensitive assets. ### AI asset categories The AI inventory is organized into the following categories to help you quickly filter your resources * **All AI Assets:** An aggregated view of all AI resources. * **Agents** AI agents (such as AWS Bedrock Agents) that use models and tools to perform tasks and assist primary models. * **Datasets:** Collections of data used by AI models. This includes Training datasets used to teach the model how to process information, and Inference datasets used to provide real-world data during the model's inference phase such as for Retrieval-Augmented Generation, or RAG. * **Models:** The trained machine learning models themselves , whether managed by a cloud provider or self-managed on your own cloud infrastructure. * **Model Endpoints:** The interface through which applications interact with the AI model, acting as an access point for sending inputs and receiving generated outputs. * **Software packages** The underlying AI-related software packages and SDKs used by developers to build these systems. ### Expanded AI asset information Clicking an AI asset in the inventory table opens a detailed asset card with specialized tabs for deep inspection: * **Overview:** Summarizes highlights, properties, and asset identifiers. * **Identity:** Provides an aggregated view of the permissions associated with the asset, mapping the relationships and displaying the identities that can access it. * **Data:** Displays data profiles such as CCPA, Financial, and PCI, as well as data patterns found within datasets related to the asset. * **AI Ecosystem:** Visualizes the "Asset Story," providing a topological map of how the AI components interact. For example, mapping the foundational Model connected to the Model Endpoint, tied to the Inference Dataset, --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://cortex-docs.paloaltonetworks.com/cortex-cloud-posture-management/cloud-inventory-and-environment/asset-classes/ai-assets.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.